Volume 02, Issue 8
Published on: August 2026
AI EXPERT SYSTEM FOR MIGRAINE DIAGNOSIS AND TREATMENT: APPLICATIONS AND RISKS
Helina Rose M Priyadharshini S Rubika B
Dr.S.Suganyadevi
Department of Computer science, Sri Krishna Arts and Science College
Article Status
Plagiarism Passed
Peer Reviewed
Open Access
Available Documents
Abstract
In the healthcare industry, artificial intelligence (AI) has grown to be a crucial tool for decision-making, especially in the areas of diagnosis, treatment planning, patient monitoring, and clinical decision support. Expert systems offer a unique approach among the various types of AI because they directly describe domain knowledge thru facts, rules, and inference procedures. The Migraine Diagnosis and Treatment Expert System (MDATES), a rule-based medical expert system created with the CLIPS production-rule environment, is the subject of a thorough case study in this paper. By analyzing clinical symptoms and connecting them with knowledge-based guidelines, MDATES is intended to help with migraine diagnosis and therapy suggestion. The approach takes into account a number of migraine symptoms and classifications, such as episodic and chronic migraine as well as specific migraine-related subtypes. The case study looks at the system's architecture, operation, applications, benefits, drawbacks, and hazards. An illustration of how expert knowledge can be converted into a computational decision-support system can be seen in the reported evaluation of MDATES. However, safe clinical implementation requires more than just strong diagnostic
performance. Incomplete or out-of-date knowledge, wrong rules, poor generalizability, automation bias, algorithmic bias, privacy threats, cybersecurity, explainability restrictions, professional responsibility, and improper use beyond of the intended clinical scope are all significant dangers. Clinical AI systems need to go via rigorous validation, human-factor evaluation, fairness assessment, privacy protection, and ongoing monitoring, according to recent research. This paper makes the case that expert systems like MDATES should not be seen as independent substitutes for medical personnel, but rather as clinical decision-support tools. Transparent knowledge management, human supervision, external validation, safety monitoring, and unambiguous accountability are all necessary for responsible deployment.
performance. Incomplete or out-of-date knowledge, wrong rules, poor generalizability, automation bias, algorithmic bias, privacy threats, cybersecurity, explainability restrictions, professional responsibility, and improper use beyond of the intended clinical scope are all significant dangers. Clinical AI systems need to go via rigorous validation, human-factor evaluation, fairness assessment, privacy protection, and ongoing monitoring, according to recent research. This paper makes the case that expert systems like MDATES should not be seen as independent substitutes for medical personnel, but rather as clinical decision-support tools. Transparent knowledge management, human supervision, external validation, safety monitoring, and unambiguous accountability are all necessary for responsible deployment.
How to Cite this Paper
M, H. R., S, P. & B, R. (2026). AI Expert System for Migraine Diagnosis and Treatment: Applications and Risks. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(8), 1-9. https://doi.org/10.55041/ijcope.v2i8.142
M, Helina, et al.. "AI Expert System for Migraine Diagnosis and Treatment: Applications and Risks." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 8, 2026, pp. 1-9. doi:https://doi.org/10.55041/ijcope.v2i8.142.
M, Helina,Priyadharshini S, and Rubika B. "AI Expert System for Migraine Diagnosis and Treatment: Applications and Risks." International Journal of Creative and Open Research in Engineering and Management 02, no. 8 (2026): 1-9. https://doi.org/https://doi.org/10.55041/ijcope.v2i8.142.
Search & Index
References
- Vasey, M. Nagendran, B. Campbell, D. A. Clifton, G. S. Collins, S. Denaxas, A. K. Denniston, L. Faes, B. Geerts, M. Ibrahim, X. Liu, B. A. Mateen,
- Mathur, M. D. McCradden, L. Morgan,
- Ordish, C. Rogers, S. Saria, D. S. W. Ting, P. Watkinson, W. Weber, P. Wheatstone, and P. McCulloch, “Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI,” Nature Medicine, vol. 28,
- 924–933, 2022.
- -C. Juang, M.-H. Hsu, Z.-X. Cai,
and C.-M. Chen, “Developing an AI-assisted clinical decision support system to enhance in-patient holistic health care,” PLOS ONE, vol. 17, no. 10, 2022.
- Festor, Y. Jia, A. C. Gordon, A. A. Faisal, I. Habli, and M. Komorowski, “Assuring the safety of AI-based clinical decision support systems: A case study of the AI Clinician for sepsis treatment,” BMJ Health & Care Informatics, vol. 29, no. 1, 2022.
- H. Park and W. C. Cha, “Application strategies for artificial intelligence-based clinical decision support system: From the simulation to the real-world,” Healthcare Informatics Research, vol. 28, no. 3, pp. 185–187,
2022.
- Vasey et al., “Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI,” BMJ, vol. 377, 2022.
- Rosenbacke, Å. Melhus, M. McKee, and D. Stuckler, “How explainable artificial intelligence can increase or decrease clinicians’ trust in AI applications in health care: Systematic review,” JMIR AI, vol. 3, 2024.
- Freyer, D. Groß, and M. Lipprandt, “The ethical requirement of explainability for AI-DSS in healthcare: A systematic review of reasons,” BMC Medical Ethics, vol. 25, no. 104, 2024.
- J. Chen, J. J. Wang, D. F. K.
Williamson, T. Y. Chen, J. Lipkova, M. Y. Lu, S. Sahai, and F. Mahmood, “Algorithmic fairness in artificial intelligence for medicine and healthcare,” Nature Biomedical Engineering, vol. 7,
- 719–742, 2023.
- Bouderhem, “Shaping the future of AI in healthcare through ethics and governance,” Humanities and Social Sciences Communications, vol. 11, no. 416, 2024.
- Ihaddouchen, S. N. R. Buijsman,
- Pozzi, D. van de Sande, A. A. Reis, R. Townsend, J. van den Hoven, D. Gommers, and M. E. van Genderen, “Responsible artificial intelligence in healthcare: A systematic review on the use of ethical principles in the development and deployment of artificial intelligence,” BMJ Digital Health & AI, 2026.
Ethical Compliance & Review Process
- •All submissions are screened under plagiarism detection.
- •Review follows editorial policy.
- •Authors retain copyright.
- •Peer Review Type: Double-Blind Peer Review
- •Published on: Aug 17 2026
CCBYNC
This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. You are free to share and adapt this work for non-commercial purposes with proper attribution.